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      Diet Supplementation with a Bioactive Pomace Extract from Olea europaea Partially Mitigates Negative Effects on Gut Health Arising from a Short-Term Fasting Period in Broiler Chickens

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          Abstract

          Simple Summary

          Plant-derived feed additives have been gaining interest as a means to maintain gut health in poultry. Recent studies have shown that fasting broilers up to 24 h triggers intestinal permeability increase and might be used as an experimental model to challenge gut health. The present study has demonstrated that feeding broiler chickens with an olive pomace extract rich in bioactive anti-inflammatory compounds do not negatively affect growth performance. Moreover, the olive pomace extract reduced some of the negative effects that a short-term fasting period induced in the intestine of broiler chickens.

          Abstract

          The effects of supplementing chicken diets with an olive pomace extract (OE) from Olea europaea on performance and gut health after a challenge of intestinal permeability (IP) increase were studied. Treatments included a control diet with no additives (CF), and diets supplemented with 100 ppm of monensin (MF) or with 500 (OE500F) and 1500 ppm (OE1500F) of an OE. At 14 d, all birds, except those allocated in a control group (CNF), were submitted to a 15.5 h short-term fasting period to induce IP increase. Fasting increased ( p < 0.05) lactulose/mannitol ratio and Alpha 1 Acid Glycoprotein concentration, and reduced ( p < 0.001) villus/crypt ratio. Moreover, a down-regulation of Claudin-1 ( p < 0.05), an up-regulation of TLR4 and IL-8 ( p < 0.05) ileal gene expression was observed in CF birds compared to CNF. OE500F treatment reduced duodenal crypt depth compared to CF ( p < 0.05; OE linear effect). Mannitol concentration and ileal IL-8 expression were reduced in OE500F compared to CF and OE1500F ( p = 0.05). Fasting challenge induced an increase in IP triggering an inflammatory response. Supplementation of OE up to 1500 ppm did not affect growth performance and alleviated some of the negative effects of the fasting challenge.

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          Most cited references 35

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          TLR4 at the Crossroads of Nutrients, Gut Microbiota, and Metabolic Inflammation.

          Obesity is accompanied by the activation of low-grade inflammatory activity in metabolically relevant tissues. Studies have shown that obesity-associated insulin resistance results from the inflammatory targeting and inhibition of key proteins of the insulin-signaling pathway. At least three apparently distinct mechanisms-endoplasmic reticulum stress, toll-like receptor (TLR) 4 activation, and changes in gut microbiota-have been identified as triggers of obesity-associated metabolic inflammation; thus, they are expected to represent potential targets for the treatment of obesity and its comorbidities. Here, we review the data that place TLR4 in the center of the events that connect the consumption of dietary fats with metabolic inflammation and insulin resistance. Changes in the gut microbiota can lead to reduced integrity of the intestinal barrier, leading to increased leakage of lipopolysaccharides and fatty acids, which can act upon TLR4 to activate systemic inflammation. Fatty acids can also trigger endoplasmic reticulum stress, which can be further stimulated by cross talk with active TLR4. Thus, the current data support a connection among the three main triggers of metabolic inflammation, and TLR4 emerges as a link among all of these mechanisms.
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            A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data.

            Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is currently viewed as the most precise technique to quantify levels of messenger RNA. Relative quantification compares the expression of a target gene under two or more experimental conditions normalized to the measured expression of a control gene. The statistical methods and software currently available for the analysis of relative quantification of RT-PCR data lack the flexibility and statistical properties to produce valid inferences in a wide range of experimental situations. In this paper we present a novel method for the analysis of relative quantification of qRT-PCR data, which consists of the analysis of cycles to threshold values (C(T)) for a target and a control gene using a general linear mixed model methodology. Our method allows testing of a broader class of hypotheses than traditional analyses such as the classical comparative C(T). Moreover, a simulation study using plasmode datasets indicated that the estimated fold-change in pairwise comparisons was the same using either linear mixed models or a comparative C(T) method, but the linear mixed model approach was more powerful. In summary, the method presented in this paper is more accurate, powerful and flexible than the traditional methods for analysis of qRT-PCR data. This new method is especially useful for studies involving multiple experimental factors and complex designs.
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              Identification and validation of housekeeping genes as internal control for gene expression in an intravenous LPS inflammation model in chickens.

               P Meyts,  S Croubels,  S Sys (2008)
              Real-time PCR has become a powerful tool for the detection of inflammatory parameters, including cytokines. Reference or housekeeping genes are used for the normalization of real-time RT-PCR results. In order to obtain reliable results, the stability of these housekeeping genes needs to be determined. In this study the stability of five genes, including beta-actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hypoxanthine phophoribosyl-transferase (HPRT), ubiquitin (UB) and glucose-6-phosphate dehydrogenase (G6PDH), was determined in a lipopolysaccharide inflammation model in chickens. beta-Actin appeared to be the most stable single gene in our model. Because the use of a single gene for normalization can lead to relatively large errors, the use of the geometric mean of multiple reference genes or normalization factor is preferred. The most stable combination for gene expression analysis in this lipopolysaccharide inflammation model in chickens is G6PDH and UB, since their correlation coefficients were 0.953 and 0.969, respectively (BestKeeper) and an M value of 0.34 and a low V(2/3) value of 0.155 (geNorm) were obtained. The use of HPRT and GAPDH should be avoided. The stable housekeeping genes, G6PDH and UB together, can be used to normalize the expression of pro-inflammatory cytokines in a lipopolysaccharide inflammation model in chickens.
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                Author and article information

                Journal
                Animals (Basel)
                Animals (Basel)
                animals
                Animals : an Open Access Journal from MDPI
                MDPI
                2076-2615
                22 February 2020
                February 2020
                : 10
                : 2
                Affiliations
                [1 ]Departamento de Producción Agraria, Universidad Politécnica de Madrid, ETS Ingeniería Agronómica, Alimentaria y de los Biosistemas, 28040 Madrid, Spain; j.herreroe@ 123456alumnos.upm.es
                [2 ]Lucta S. A., Innovation Division, UAB Research Park, Edifici Eureka, 08193 Bellaterra, Spain; marta.blanch@ 123456lucta.com (M.B.); jose.pastor@ 123456lucta.com (J.J.P.)
                Author notes
                [* ]Correspondence: david.menoyo@ 123456upm.es ; Tel.: +34-91-454-900
                Article
                animals-10-00349
                10.3390/ani10020349
                7070366
                32098336
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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